LlamaIndex

Parses 50+ unstructured document types (PDFs, Office docs, images) using VLM-powered agentic OCR that preserves document layout, tables, charts, and handwritten text. The system uses multi-step extraction agents with auto-correction loops to handle complex layouts and embedded images, outputting structured bounding box coordinates and semantic document sections rather than raw text.

Extracts structured data from unstructured documents using LLM-powered extraction agents that operate against user-defined schemas. The system takes a document and a schema definition (e.g., JSON schema for invoice fields), then uses agentic reasoning to locate, validate, and extract matching data with type coercion and error handling, supporting multi-step extraction workflows with context awareness across document sections.

Provides document agents that perform multi-step reasoning over documents using chain-of-thought patterns and context management. Agents can decompose complex document understanding tasks into sub-steps (e.g., 'find all liability clauses, then summarize their impact'), maintain context across steps, and make decisions about which document sections to examine based on task requirements, enabling sophisticated document analysis without explicit step-by-step instructions.

Processes large document collections in batch mode with cost optimization strategies including credit pooling, rate limit management, and processing prioritization. The system batches requests to reduce overhead, manages credit consumption across multiple documents, and provides cost estimation and optimization recommendations to minimize LlamaParse credit usage while maintaining processing quality.

Classifies documents into categories using natural-language rule definitions interpreted by LLMs, rather than requiring explicit regex or code-based rules. Users define classification rules in plain English (e.g., 'Invoice if contains invoice number and total amount'), and the system uses agentic reasoning to apply these rules to parsed documents, supporting multi-label classification and confidence scoring.

Splits parsed documents into logical chunks optimized for RAG and embedding pipelines, using semantic awareness rather than naive character or token-based splitting. The system understands document structure (sections, paragraphs, tables) and creates chunks that preserve semantic boundaries, supporting configurable chunk size, overlap, and metadata attachment for retrieval context.

Orchestrates multi-step document processing pipelines (parse → extract → split → classify → index) using LlamaAgents/Workflows framework with support for conditional branching, error handling, and context passing between steps. The system manages state across steps, handles failures gracefully, and supports both sequential and parallel execution patterns for complex document automation workflows.

Builds complete RAG (Retrieval-Augmented Generation) systems with enterprise-grade document chunking, embedding, and vector storage integration. The system handles the full pipeline: document parsing → semantic chunking → embedding generation → vector store indexing → retrieval with ranking, supporting multiple vector databases and embedding models with configurable retrieval strategies.

Provides cloud-hosted document processing via LlamaParse SaaS with credit-based pricing model (1,000 credits = $1.25 USD). Users pay per-document based on processing complexity: basic parsing costs fewer credits than layout-aware agentic parsing. Free tier includes 10,000 credits/month (~1,000 pages), with paid tiers up to 4,000K credits/month, supporting on-demand scaling without infrastructure management.

Ingests documents from multiple cloud storage and collaboration platforms (S3, Azure Blob, OneDrive, SharePoint, Box, Google Drive, Confluence) with native connectors that handle authentication, pagination, and incremental updates. The system automatically discovers documents, manages credentials securely, and supports batch processing of large document collections without manual file management.

LiteParse provides open-source, local-only document parsing for PDFs, Office documents, and images without cloud connectivity or LLM token consumption. The system outputs bounding box coordinates and text extraction with layout preservation, enabling offline document processing and cost-free parsing for applications that don't require LLM-powered extraction or agentic reasoning.

Supports enterprise deployment models including VPC (Virtual Private Cloud) deployment on AWS and Azure marketplaces for on-premises processing, and hybrid cloud configurations combining cloud and on-premises infrastructure. The system maintains SOC 2 Type II compliance, data encryption in transit and at rest, and enterprise SSO for access control, enabling organizations to meet strict data residency and security requirements.